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UID:6252@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20211126T143000
DTEND;TZID=Europe/Paris:20211126T153000
DTSTAMP:20241120T201347Z
URL:https://www.i2m.univ-amu.fr/evenements/registration-of-point-clouds-wi
 th-anisotropic-localization-noise-for-single-particle-reconstruction-in-fl
 uorescence-microscopy/
SUMMARY:Denis FORTUN (ICube - MIV\, Université de Strasbourg\, Illkirch): 
 Registration of point clouds with anisotropic localization noise for singl
 e particle reconstruction in fluorescence microscopy
DESCRIPTION:Denis FORTUN: Recent advances in super-resolution techniques ha
 ve greatly improved the resolution of fluorescence microscopy. However\, t
 his progress is still hampered by resolution anisotropy and partiallabelli
 ng issues. In this talk we will address these limitations in the single pa
 rticle reconstruction paradigm. The idea is to perform multi-view reconstr
 uction of a given biological particle from 3D images containing hundreds o
 f copies this particle withunknown poses. We will first give an overview o
 f the current challenges in SPR at each step of the reconstruction pipelin
 e.\nWe will then focus on the pose estimation problem in single molecule l
 ocalization microscopy (SMLM)\, which provides data in the form of point c
 louds corrupted with high anisotropic localizationnoise. Our approach foll
 ows the framework of reconstruction of a Gaussian mixture model (GMM) with
  an expectation-maximization (EM) algorithm. Contrary to existing methods 
 that implicitly assume isotropic Gaussian noise\, we introduce an explicit
  localizationnoise model that decouples shape modeling with the the GMM fr
 om noise handling. We design a stochastic EM algorithm that considers nois
 e-free data as a latent variable\, with closed-form solutions at each EM s
 tep. The first advantage of our approach is to handlespace-variant and ani
 sotropic Gaussian noise with arbitrary covariances. The second advantage i
 s to leverage the explicit noise model to impose prior knowledge about the
  noise available in SMLM. \n&nbsp\;\n\n&nbsp\;\n\n&nbsp\;
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 021/10/Denis_Fortun.jpg
CATEGORIES:Séminaire,Signal et Apprentissage
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